In the second half of the nineteenth century, the majority of U.S. states adopted a novel code of legal practice for their civil courts. Legal scholars have long recognized the influence of the New York lawyer David Dudley Field on American legal codification, but tracing the influence of Field’s code of civil procedure with precision across some 30,000 pages of statutes is a daunting task. By adapting methods of digital text analysis to observe text reuse in legal sources, this article provides a methodological guide to show how the evolution of law can be studied at a macro level—across many codes and jurisdictions—and at a micro level—regulation by regulation. Applying these techniques to the Field Code and its emulators, we show that by a combination of creditors’ remedies the code exchanged the rhythms of agriculture for those of merchant capitalism. Archival research confirmed that the spread of the Field Code united the American South and American West in one Greater Reconstruction. Instead of just a national political development centered in Washington, we show that Reconstruction was also a state-level legal development centered on a procedure code from the Empire State of finance capitalism.
The authors’ original manuscript (or preprint) is available at SSRN. This is the version that we submitted for peer review in July 2016. The final version will be different, in part because of our revisions in response to the helpful peer reviews, and in part because we have expanded our original corpus by some 40% and plan to expand it further before publication. While we think these revisions greatly strengthen the essay, we don’t think that they invalidate this earlier version. So we are making the authors’ original manuscript available now following Oxford University Press’s policy.
In his elegantly written account, Kyle Roberts takes his readers on a tour of Evangelical Gotham. The book has a strong chronological through line, explaining how evangelicals went through three distinct periods in bringing their message of conversion and reform to New York City (10–11). While the spatial organization of the book is less obvious from its table of contents, Evangelical Gotham is a book that is fundamentally organized around place. This may seem like an obvious point to make about a book that focuses on a single city, but my aim is to show how Roberts uses spatial concepts.
Evangelical Gotham is explicit in its debt to the concept of “crossing and dwelling” articulated by Thomas Tweed. Roberts makes this clear in his first chapter, where he writes about spiritual autobiographies at the end of the eighteenth and beginning of the nineteenth centuries. He takes a fresh approach to this topic by giving conversion narratives a meaning both in geographic and spiritual space. Evangelicals crossed religious boundaries by converting, but many of them did so at the same time that they were crossing the ocean or moving to the city. And once they arrived in New York, these newly converted evangelicals had to dwell not just in the city but also had to find a church or “community of faith” (27).
In September of 1861, the U.S. Coast Survey published a large map, just under three feet square, titled a “Map showing the distribution of the slave population of the southern states of the United States.” Based on the population statistics gathered in the 1860 census, and certified by the superintendent of the Census Office, the map depicted the percentage of the population enslaved in each county.
The map showed at a glance the large-scale patterns of slavery in the American South: the concentrations of slavery in eastern Virginia, in South Carolina, and most of all along the Mississippi. It also repaid closer examination, since each county was labeled with the exact percentage enslaved. The map of slavery was one of many thematic maps produced in the nineteenth century United States. As Susan Schulten has shown, this particular map was used by the federal government during the Civil War, and it was a favorite of Abraham Lincoln’s.1
Though such thematic maps, in particular of slavery, have their origins in the nineteenth century, the technique is useful for historians. As I see it, one of the main problems for the historians’ method today is the problem of scale. How can we understand the past at different chronological and geographical scales? How can we move intelligibly between looking at individuals and looking at the Atlantic World, between studying a moment and studying several centuries?2 Maps can help, especially interactive web maps that make it possible to zoom in and out, to represent more than one subject of interest, and to set representations of the past in motion in order to show change over time.
TL;DR I made a bad map of slavery, and there is a better map at the end of the post.
When I finished working the other night I tweeted the current state of the map of slavery that I had been making. Anthea Butler retweeted it, and then a lot of people saw it. (Not that many, but certainly more than will ever read the dissertation chapter the map is a part of.) I’m glad that people found the map interesting. But though there was nothing erroneous about the map, it certainly was not the best map of slavery possible. Here is the draft map.
It’s easy to spot the biggest problem in that map: the values mapped to the colors are less than ideal. I suspect that most people who saw the map didn’t pay any attention to the legend at the bottom. And why should they have? Until I changed the numbers to a humanist-readable format the legend was almost incomprehensible. What the legend means is that the lightest yellow represents counties where there were 450 or fewer slaves living; the dark red represents counties where there were more than 5,380 slaves and fewer than 37,300 slaves.
Those numbers should give a reader pause: why should a county with 5,380 slaves be classified the same as a county with almost seven times as many slaves? The breaks in the first map are not arbitrary, but divide the counties into quartiles. That is, I ran a function which divided the counties into four even groups. This was a rough and ready way to classify the counties.
The trouble is that quartiles are not a particularly meaningful way to classify the counties. You might even argue—and this certainly wasn’t my intent—that it is a sensationalist way to classify the counties. By definition, using quartiles means that one-quarter of the counties on the map would be colored bright red. If this were a map of smokers, then one-fourth of the counties would be bright red; if it were a map of lung cancer, one-fourth of the counties would be bright red. That’s because when using quartiles, the breaks are determined by the count of the observations (i.e., the number of counties) rather than the value of the observations (i.e., the number of slaves in each county). Below is a histogram of the distribution of counties: you can see that a few counties had very large numbers of slaves, while most counties had relatively smaller numbers.
But the question of how to categorize the counties is as much a historical question as it is a question for the techniques of data analysis. Though histories of slavery have often been written about large plantations where many slaves lived, historians have long known that many enslaved African Americans did not live on plantations, because most slaveholders owned only a few slaves. This is an important point, because the possibilities for slave culture and religion are very different on a farm with one or two enslaved African Americans than on a plantation with a hundred slaves. Below is a chart of the number of slaveholders by the number of slaves that they owned. 1 (Notice also how widespread slave ownership was: 395,216 slaveholders according to the 1860 census.)
What the charts of counties and slaveholders demonstrate is that dividing the counties into quartiles does not make for an accurate map. Fortunately there are better methods, in particular George Jenks’s algorithm for finding breaks in the data set. The Jenks method tries to make groups whose individual members are as close to each other as possible, but where each group in the aggregate is as much unlike the other groups as possible. Using that algorithm, we can divide the counties into more meaningful groups, as the chart below shows.
Using the Jenks breaks, we get a much better map of where slaves lived in 1860. We can see all of the detail that was in the earlier map: the South’s fertile crescent through Virginia, North Carolina, South Carolina, George, Alabama, and Mississippi; the Mississippi, Missouri, and Tennessee river valleys; South Carolina as the state with the highest concentration of slaves. But this revised map has a higher resolution (if you will). We can now see cities like Washington, Charleston, Nashville, Mobile, and New Orleans—important since slavery must be understood in terms of slave markets, commodities markets, and capitalism. 2 The hinterland of slavery is also more clearly defined—important since the expansion of slavery was the issue in the sectional crisis. 3
The lesson here is not that you should only make finished work public. But I hope that this look at the decisions that go into working with data demonstrates how a historian’s knowledge is more important than technological skill in making a historical map.
In this case the categories come directly from the Census tables. As some people wrote to say, the proportion of African Americans in the total population is another way to measure this, but that’s the subject of another map.↩
See Walter Johnson, Soul by Soul: Life Inside the Antebellum Slave Market (Cambridge, MA: Harvard University Press, 1999); Walter Johnson, River of Dark Dreams: Slavery and Empire in the Cotton Kingdom, 2013.↩